30 AI and Crypto Narratives
AI and Crypto - So hot right now
Random Observation/Comment #863: It seems like everyone just wants to work less and live more.
//Made with Meta AI. It knows my face!
Why this List?
Let’s give the AI Agents unique crypto wallets, funds, permissions to sign transactions, market research for meme coins, ability to launch meme coins, ability to mass create other AI Agents, and give them the goal to make money — What can go wrong?
Honestly, most of the ideas for a self-regulating treasury/DAO/trust/financial advisor and robo-advisor for trading has been explored a decade ago, but now you can just do it faster and more irresponsibly while blaming an immature black box technology that has no accountability when it’s open source. Use at your own risk.
I wrote this list because I just so happen to be the epitome of tech bro crypto AI finance geek. It’s okay - I self identify and wear my persona proudly. At least I don’t say “LFG!” all the time. Crypto AI FTW.
UI/UX Integration of LLMs - LLMs will quietly blend into everyday interfaces, compressing info down to contextually relevant snippets embedded next to emails, documents, and code windows. Centralized endpoints remain the norm, but now with a conversational layer that streamlines user workflows.
Action Models & API Connectivity - Instead of just generating text, LLMs gain the ability to execute API calls—reading Slack logs, running web searches, or signing a wallet transaction. AI Agents become orchestrators that move beyond static prompts into fully capable operational units.
On-Chain LLM Training Data - While appealing in theory, most on-chain LLM data storage is impractical. The blockchain’s inefficiency and cost structure make it far easier to rely on off-chain analytics and APIs. Talking to your on-chain portfolio might sound cool, but simpler architectures are cheaper and more direct. Keep things you don’t need to share/prove off-chain.
Payments for Training Data Contributions - Crypto rails promise trustless micropayments for data providers, but enforcement is still the sticking point. If a data marketplace can align incentives and minimize free-riding, training data could earn its keep through Web3’s built-in financial primitives. I’m still in the opinion that the whole internet has already been learned and mined for data. What’s next is all the allowed personalized IOT data.
AI-Enhanced Web3 Capabilities - Just as ChatGPT integrated code generation and image creation, we’ll see LLM-enabled platforms trigger Phosphor NFT minting, spin up DAOs, or modify smart contracts. This on-chain AI creator concept unlocks new primitives, but complexity and risk are everywhere.
On-Chain LLM Storage Trade-Offs - Storing model artifacts or conversation logs directly on-chain is expensive and slow. Hybrid solutions—storing proofs, references, or signatures on-chain while keeping bulk data off-chain—will strike a balance between trust and efficiency.
AI On-Chain Identities - Just as ENS gives human-readable crypto addresses, we could grant AIs unique on-chain identities with wallets and permissions. Imagine an AI agent DAO that’s fully auditable, operating within predefined rulesets, and wielding funds on behalf of a community.
AI-Driven DAO Governance - DAO proposals could be pre-processed by LLMs that summarize complex documents, highlight risks, and even simulate likely outcomes. Over time, DAOs may delegate more routine decisions to AI executors that follow the on-chain logic spelled out by token holders.
Fraud Detection & Compliance via AI - AI models fine-tuned with on-chain transaction graphs can spot suspicious activity, front-running patterns, and market manipulations (this is more pattern recognition with supervised learning). Running these analyses on decentralized data sets can strengthen trust without requiring centralized intermediaries.
On-Chain Authenticity & Provenance - LLMs can analyze and verify the authenticity of NFT metadata, bridging AI’s pattern detection with crypto’s immutability. Cross-checking provenance through on-chain signatures and AI detection models can help distinguish real digital art from low-effort copies.
Tokenized Data Sets for Model Training - Instead of freely floating datasets, imagine token-gated data pools where model trainers must buy in. Access to specialized data becomes a digital asset, and model creators who respect usage constraints can be rewarded or penalized via smart contracts. I think enforcement will be difficult here.
Zero-Knowledge Model Introspection - Using ZK-proofs (ZK Machine Learning trend), it’s possible to prove that a model holds certain properties (like fairness constraints) without revealing its weights. This privacy-preserving mechanism could become core to trusted AI verification in decentralized contexts.
Compute Marketplaces on-Chain - Instead of depending on a single AI provider, imagine a marketplace where you bid on GPU resources via smart contracts. This decentralization could lower costs, increase accessibility, and ensure a healthier competitive landscape for AI model training and inference. From my view, decentralization is usually more expensive, but we do give solo/independent participants some earning opportunity.
Interoperable AI Frameworks via Smart Contracts - Standardized smart contracts might define AI model interfaces that multiple providers can plug into. This ensures a modular, composable architecture where AI capabilities become just another building block in the crypto ecosystem. There’s already some great toolkits out there. Check out app.autono.meme
Federated Learning with Crypto Incentives - Train models collaboratively across multiple parties, each rewarded for their data contributions with tokens. Cryptoeconomic incentives ensure honest participation, while federated protocols keep sensitive data at the edges.
AI-Driven Stablecoin Rebalancing - Model-driven insights into global currency markets or liquidity pools can inform on-chain stablecoin rebalancing strategies. AIs could dynamically adjust collateral reserves, algorithmically improving stability. Let’s also not just put “AI” in front of or in replacement to “algorithms.”
Cross-Chain LLM Orchestration - As multi-chain ecosystems flourish, LLMs could become orchestrators that read from multiple blockchains, reconcile differences, and provide unified insights. This cross-chain intelligence could simplify user experiences in a fragmented ecosystem.
AI-Powered NFT Curation - LLMs and generative models can curate and evolve NFT collections over time. NFTs can become living artifacts that adapt based on AI-analyzed social sentiment, collector tastes, or market conditions. I could also see an NFT get generatively merged with other NFTs in your portfolio.
Open-Source Model Development DAOs - Communities could pool funds to train open-source LLMs, governed by token votes. The resulting models become public goods, with usage rights, fees, and updates all codified via on-chain governance. Perhaps this is a foundation-based AI Agent.
Automated Contract Audits - AI-assisted code reviews and vulnerability detection could become standard. Smart contracts might undergo automated scans by models trained on previous exploits—reducing risk, improving trust, and accelerating development cycles.
AI-Enhanced DeFi Strategy Recommendation - Instead of users manually chasing yield strategies or liquidity positions, AI agents analyze market conditions and automatically suggest optimal paths. Over time, these agents might actively manage user funds on-chain.
Secure Multi-Party Computation (MPC) for AI Models - MPC techniques allow multiple participants to train or query AI models without revealing raw data. Combined with crypto incentives, this encourages secure collaboration among competing stakeholders.
Encrypted Prompt Protocols - Beyond the model’s output, even prompts and instructions can be encrypted. This can ensure that both user queries and the AI’s logic remain private, with only cryptographic proofs of compliance stored on-chain.
Decentralized Recommendation Engines - Instead of relying on a single platform’s recommendation algorithms, decentralized protocols could allow multiple AI models to compete, providing diverse suggestions for products, art, or investment opportunities.
Model Weight Tokenization - Tokenizing model weights turns them into digital assets that can be bought, sold, or rented. This transforms models into composable liquidity pools of intelligence, potentially unlocking fluid markets for specialized AI capabilities.
AI-Based Network State Predictions - AI analytics on on-chain data can forecast network states, congestion levels, or validator reliability, helping node operators and users anticipate and adapt to shifting blockchain conditions.
Encrypted AI Consultations - Privacy-focused protocols could allow users to consult specialized LLMs about sensitive topics (like tax or legal advice) without revealing their identity or data, ensuring trust and confidentiality.
On-Chain IP Licensing with AI Arbitration - Smart contracts could handle IP licenses for AI-generated content, with LLMs acting as neutral arbiters to interpret license clauses. Token-based enforcement could settle disputes automatically.
Dynamic Reputation Systems - AI scoring of user behaviors, governance contributions, and protocol interactions could yield reputation scores. These can become on-chain badges that reflect trustworthiness or expertise—fueling new social and economic layers.
Bridging AI, AR/VR, and On-Chain Data - As the metaverse matures, LLMs integrated with on-chain assets can personalize immersive experiences. From adjusting environments based on user-owned NFTs to shaping AR overlays driven by AI insights, we’ll see hybrid ecosystems that mesh virtual, on-chain, and intelligent layers seamlessly.
~See Lemons Combined AI and Crypto
